Senior Platform Engineer – Agentic AI & Harness Engineering

Kyndryl; Madrid; Presencial; EUR 60.000 - 80.000; Jornada completa

Descripción de la vacante

Kyndryl is seeking a skilled individual to design and build an agent harness to integrate systems and enhance productivity. With a focus on collaboration and measurable results, this role requires a minimum of 5 years in Platform Engineering and expertise in Python, Kubernetes, and cloud services.

Fluency in English and Spanish is essential. Join Kyndryl and contribute to innovative solutions in a rapidly evolving tech landscape.

Formación

• 5+ years in Platform Engineering, DevOps, or SRE.
• Hands-on experience building agent harnesses or agentic systems.
• Solid software engineering skills, primarily in Python.
• Strong cloud-native experience with major cloud providers.

Responsabilidades

• Design and build the agent harness with runtime and orchestration.
• Integrate agents with the systems they act on through APIs.
• Mentor teams and run enablement to facilitate agent adoption.

Conocimientos

Platform Engineering

DevOps

SRE

Python

Kubernetes

Infrastructure as Code

AWS

GCP

Spanish

English

Descripción del empleo

At Kyndryl, we run and reimagine the mission‑critical technology systems that drive advantage for the world’s leading businesses. We are at the heart of progress; we use proven expertise and a continuous flow of AI‑powered insight to enable smarter decisions, faster innovation, and a lasting competitive edge.

The Role

What You Will Do

• Design and build the agent harness: runtime, orchestration, tool and MCP integration, context and memory, evaluation, guardrails and observability.
• Build golden paths and reusable patterns so teams ship agents that are reliable, governed and measurable, instead of one‑off scripts.
• Define the oversight model: policy‑as‑code, human‑in‑the‑loop checkpoints, loop and failure detection, evaluation harnesses, and cost control.
• Integrate agents with the systems they act on: CI/CD, infrastructure, cloud services, observability and ticketing, through APIs and MCP.
• Make agentic productivity measurable: define what productive means for each use case, instrument it, and run the harness on that data.
• Keep the harness model‑agnostic and versioned, so it survives model upgrades and provider changes without rewrites.
• Mentor teams, run enablement, and reduce friction between Dev, Ops, Security and the people adopting agents.

What Success Looks Like In Your First Year

• Teams ship agents through the harness golden paths instead of bespoke scripts.
• Agentic workflows run in production with guardrails, evaluation and measured productivity impact.
• The harness absorbs at least one model or provider upgrade without a rewrite.

Who You Are

What you will bring

• 5+ years in Platform Engineering, DevOps or SRE, building developer platforms or runtime and orchestration systems in production.
• Hands‑on experience building agent harnesses or agentic systems: runtime, tool and MCP integration, orchestration, evaluation and guardrails.
• Solid software engineering, primarily in Python, with the depth to build production systems and abstractions, not only prompts.
• Strong cloud‑native background: Kubernetes, containers, Infrastructure as Code, and one major cloud (AWS, Azure or GCP).
• A working grasp of how LLM agents fail and the engineering that makes them reliable: context design, loop and failure detection, verification and evaluation.
• Experience treating a platform as a product: contracts, SLOs, adoption metrics and real users.
• Professional working English and Spanish.

Nice to have

• Agent frameworks such as LangGraph, CrewAI, AutoGen etc. and the Model Context Protocol.
• Serious production use of AI‑assisted development tools (Claude Code, Copilot, Codex).
• LLMOps, agent evaluation, or observability for agentic systems.
• Prior work on harnesses that stay stable across model upgrades.
• Platform engineering in regulated environments (finance, insurance, public sector).